graphengine/ge/graph/passes/infer_value_range_pass.cc

/**
 * Copyright 2020 Huawei Technologies Co., Ltd
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

#include "graph/passes/infer_value_range_pass.h"
#include "common/util/error_manager/error_manager.h"
#include "framework/common/debug/ge_log.h"
#include "graph/debug/ge_attr_define.h"
#include "graph/operator_factory_impl.h"
#include "graph/passes/constant_folding_pass.h"
#include "graph/utils/type_utils.h"
#include "common/ge/ge_util.h"

using std::unique_ptr;
namespace ge {
namespace {
#define GET_DATA_BY_DTYPE(DTYPE, TYPE)                                                 \
  case (DTYPE):                                                                        \
    ConstructValueRange<TYPE>(lower_tensor, higher_tensor, output_tensor_value_range); \
    break;

Status RunCpuKernelForValueRange(NodePtr &node, const vector<ConstGeTensorPtr> &inputs,
                                 std::vector<GeTensorPtr> &outputs) {
  // should use RunOpKernelWithCheck, RunOpKernel for ut test
  auto ret = ConstantFoldingPass::RunOpKernel(node, inputs, outputs);
  if (ret != SUCCESS) {
    auto op_kernel = folding_pass::GetKernelByType(node);
    if (op_kernel == nullptr) {
      GELOGW("Calculate value range failed, no op kernel for node %s type %s", node->GetName().c_str(),
             node->GetType().c_str());
      return NOT_CHANGED;
    }

    ret = op_kernel->Compute(node->GetOpDesc(), inputs, outputs);
    if (ret != SUCCESS) {
      GELOGW("Calculate for node %s failed in constant folding", node->GetName().c_str());
      return NOT_CHANGED;
    }
  }
  GELOGI("Node %s type %s, run cpu kernel success.", node->GetName().c_str(), node->GetType().c_str());
  return SUCCESS;
}
}  // namespace

graphStatus InferValueRangePass::Infer(NodePtr &node) {
  PrintInOutTensorShape(node, "before_infer_value_range");
  auto infer_value_range_param = OperatorFactoryImpl::GetInferValueRangePara(node->GetType());

  // Use registered func to calculate value range
  if (!infer_value_range_param.use_cpu_kernel) {
    if (infer_value_range_param.infer_value_func == nullptr) {
      GELOGW("The registered func of node %s to infer value range is nullptr.", node->GetName().c_str());
      return GRAPH_NOT_CHANGED;
    }
    Operator op = OpDescUtils::CreateOperatorFromNode(node);
    auto ret = node->GetOpDesc()->CallInferValueRangeFunc(op);
    if (ret != GRAPH_SUCCESS) {
      GELOGW("Node %s call infer value range func failed, ret: %u.", node->GetName().c_str(), ret);
      return GRAPH_NOT_CHANGED;
    }
    return GRAPH_SUCCESS;
  }

  // Use CPU kernel func to calculate value range
  auto ret = ConstructInputAndInferValueRange(node);
  if (ret != GRAPH_SUCCESS) {
    GELOGW("Use CPU kernel to calculate value range failed. node: %s, ret: %u", node->GetName().c_str(), ret);
    return GRAPH_NOT_CHANGED;
  }
  return GRAPH_SUCCESS;
}

bool InferValueRangePass::NeedInfer(const NodePtr &node) {
  auto infer_value_range_param = OperatorFactoryImpl::GetInferValueRangePara(node->GetType());
  if (!infer_value_range_param.is_initialized) {
    GELOGD("Node %s does not register func to infer value range, skip infer_value_range_pass.",
           node->GetName().c_str());
    return false;
  }

  if (infer_value_range_param.when_call == INPUT_IS_DYNAMIC) {
    // Only do infer for node that all inputs are dynamic, such as shape
    if (InputIsDynamic(node)) {
      return true;
    }
    GELOGD("Node %s register func to infer value range and when_call is INPUT_IS_DYNAMIC, but check input failed.",
           node->GetName().c_str());
  } else if (infer_value_range_param.when_call == INPUT_HAS_VALUE_RANGE) {
    // Only do infer for node that all inputs have value_range or node type of inputs is constant/const
    if (InputIsConstOrHasValueRange(node)) {
      return true;
    }
    GELOGD("Node %s register func to infer value range and when_call is INPUT_HAS_VALUE_RANGE, but check input failed.",
           node->GetName().c_str());
  }
  GELOGD("Node %s does not need to infer value range, skip infer_value_range_pass.", node->GetName().c_str());
  return false;
}

bool InferValueRangePass::InputIsDynamic(const NodePtr &node) {
  bool input_is_dynamic = false;
  auto cur_op_desc = node->GetOpDesc();
  for (const auto &input_desc : cur_op_desc->GetAllInputsDescPtr()) {
    auto dims = input_desc->GetShape().GetDims();
    for (auto dim : dims) {
      if (dim == UNKNOWN_DIM || dim == UNKNOWN_DIM_NUM) {
        input_is_dynamic = true;
        break;
      }
    }
  }
  return input_is_dynamic;
}

bool InferValueRangePass::InputIsConstOrHasValueRange(const NodePtr &node) {
  bool input_is_const_or_has_value_range = true;
  auto cur_op_desc = node->GetOpDesc();
  auto in_data_anchors = node->GetAllInDataAnchors();
  for (size_t i = 0; i < in_data_anchors.size(); ++i) {
    auto peer_out_anchor = in_data_anchors.at(i)->GetPeerOutAnchor();
    if (peer_out_anchor == nullptr) {
      continue;
    }
    auto peer_node = peer_out_anchor->GetOwnerNode();
    if (peer_node == nullptr || peer_node->GetOpDesc() == nullptr) {
      continue;
    }
    if ((peer_node->GetType() == CONSTANT) || (peer_node->GetType() == CONSTANTOP)) {
      continue;
    }

    const auto &input_desc = cur_op_desc->GetInputDesc(i);
    std::vector<std::pair<int64_t, int64_t>> value_range;
    (void)input_desc.GetValueRange(value_range);
    if (value_range.empty()) {
      GELOGD("Node %s input %zu does not have value range, skip infer_value_range_pass for current node.",
             node->GetName().c_str(), i);
      input_is_const_or_has_value_range = false;
      break;
    }
  }
  return input_is_const_or_has_value_range;
}


bool InferValueRangePass::SameTensorDesc(const GeTensorDescPtr &src, const GeTensorDescPtr &dst) {
  bool same_desc = true;
  std::vector<std::pair<int64_t, int64_t>> src_value_range;
  std::vector<std::pair<int64_t, int64_t>> dst_value_range;
  (void)src->GetValueRange(src_value_range);
  (void)dst->GetValueRange(dst_value_range);
  if (src_value_range != dst_value_range) {
    same_desc = false;
  }
  return same_desc;
}

graphStatus InferValueRangePass::UpdatePeerInputDesc(const GeTensorDescPtr &src, GeTensorDescPtr &dst, bool &changed) {
  changed = false;
  std::vector<std::pair<int64_t, int64_t>> src_value_range;
  std::vector<std::pair<int64_t, int64_t>> dst_value_range;
  (void)src->GetValueRange(src_value_range);
  (void)dst->GetValueRange(dst_value_range);
  if (src_value_range != dst_value_range) {
    changed = true;
  }

  dst->SetValueRange(src_value_range);
  return GRAPH_SUCCESS;
}

template <typename T>
graphStatus InferValueRangePass::ConstructData(const GeTensorDesc &tensor_desc, bool use_floor_value,
                                               GeTensorPtr &output_ptr) {
  std::vector<std::pair<int64_t, int64_t>> value_range;
  (void)tensor_desc.GetValueRange(value_range);
  if (static_cast<int64_t>(value_range.size()) != tensor_desc.GetShape().GetShapeSize()) {
    REPORT_INNER_ERROR("E19999", "Value range of input %s is invalid.", tensor_desc.GetName().c_str());
    GELOGE(GRAPH_PARAM_INVALID, "Value range of input %s is invalid.", tensor_desc.GetName().c_str());
    return GRAPH_PARAM_INVALID;
  }

  size_t value_range_data_num = value_range.size();
  unique_ptr<T[]> buf(new (std::nothrow) T[value_range_data_num]());
  if (buf == nullptr) {
    REPORT_INNER_ERROR("E19999", "New buf failed");
    GELOGE(MEMALLOC_FAILED, "new buf failed");
    return GRAPH_FAILED;
  }
  for (size_t j = 0; j < value_range_data_num; ++j) {
    auto value_range_j = use_floor_value ? value_range[j].first : value_range[j].second;
    buf[j] = static_cast<T>(value_range_j);
  }

  if (output_ptr->SetData(reinterpret_cast<uint8_t *>(buf.get()), value_range_data_num * sizeof(T)) != GRAPH_SUCCESS) {
    GELOGE(GRAPH_FAILED, "set data failed");
    return GRAPH_FAILED;
  }
  return GRAPH_SUCCESS;
}

graphStatus InferValueRangePass::ConstructDataByType(const GeTensorDesc &tensor_desc, bool use_floor_value,
                                                     GeTensorPtr &output_ptr) {
  graphStatus ret = GRAPH_SUCCESS;
  auto data_type = tensor_desc.GetDataType();
  output_ptr->MutableTensorDesc().SetDataType(data_type);
  switch (data_type) {
    case DT_FLOAT:
      ret = ConstructData<float>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_DOUBLE:
      ret = ConstructData<double>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_UINT8:
      ret = ConstructData<uint8_t>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_INT8:
      ret = ConstructData<int8_t>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_UINT16:
      ret = ConstructData<uint16_t>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_INT16:
      ret = ConstructData<int16_t>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_INT32:
      ret = ConstructData<int32_t>(tensor_desc, use_floor_value, output_ptr);
      break;
    case DT_INT64:
      ret = ConstructData<int64_t>(tensor_desc, use_floor_value, output_ptr);
      break;
    default:
      GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str());
      ret = GRAPH_FAILED;
  }
  return ret;
}

vector<ConstGeTensorPtr> InferValueRangePass::ConstructInputTensors(const NodePtr &node, bool use_floor_value) {
  vector<ConstGeTensorPtr> input_tensors;
  auto cur_op_desc = node->GetOpDesc();
  auto in_data_anchors = node->GetAllInDataAnchors();
  for (size_t i = 0; i < in_data_anchors.size(); ++i) {
    auto peer_out_anchor = in_data_anchors.at(i)->GetPeerOutAnchor();
    if (peer_out_anchor == nullptr) {
      continue;
    }
    auto peer_node = peer_out_anchor->GetOwnerNode();
    if (peer_node == nullptr) {
      continue;
    }

    // construct input tensor by constant node
    if ((peer_node->GetType() == CONSTANT) || (peer_node->GetType() == CONSTANTOP)) {
      vector<GeTensorPtr> const_weight = OpDescUtils::MutableWeights(peer_node);
      if (const_weight.empty()) {
        REPORT_INNER_ERROR("E19999", "MutableWeights failed, weight is empty, node: %s(%s)",
                           peer_node->GetName().c_str(), peer_node->GetType().c_str());
        GELOGE(INTERNAL_ERROR, "MutableWeights failed, weight is empty, node: %s(%s)", peer_node->GetName().c_str(),
               peer_node->GetType().c_str());
        return vector<ConstGeTensorPtr>();
      }
      // const/constant op has only one weight
      if (const_weight.at(0) == nullptr) {
        REPORT_INNER_ERROR("E19999", "MutableWeights failed, weight of constant is null, node: %s(%s)",
                           peer_node->GetName().c_str(), peer_node->GetType().c_str());
        GELOGE(INTERNAL_ERROR, "MutableWeights failed, weight of constant is null, node name: %s(%s)",
               peer_node->GetName().c_str(), peer_node->GetType().c_str());
        return vector<ConstGeTensorPtr>();
      }
      input_tensors.push_back(const_weight.at(0));
      continue;
    }

    // construct input tensor by boundary of value range
    const auto &input_tensor_desc = cur_op_desc->GetInputDesc(i);
    GeTensorPtr tmp_tensor_ptr = MakeShared<GeTensor>(input_tensor_desc);
    if (tmp_tensor_ptr == nullptr) {
      REPORT_INNER_ERROR("E19999", "Make shared failed");
      GELOGE(MEMALLOC_FAILED, "Make shared failed");
      return vector<ConstGeTensorPtr>();
    }

    auto ret = ConstructDataByType(input_tensor_desc, use_floor_value, tmp_tensor_ptr);
    if (ret != GRAPH_SUCCESS) {
      REPORT_INNER_ERROR("E19999", "Input %s construct input tensor by boundary of value range failed.",
                         input_tensor_desc.GetName().c_str());
      GELOGE(GRAPH_PARAM_INVALID, "Input %s construct input tensor by boundary of value range failed.",
             input_tensor_desc.GetName().c_str());
      return vector<ConstGeTensorPtr>();
    }
    input_tensors.push_back(tmp_tensor_ptr);
  }

  return input_tensors;
}

graphStatus InferValueRangePass::ConstructInputAndInferValueRange(NodePtr &node) {
  auto inputs = ConstructInputTensors(node, true);
  if (inputs.empty()) {
    return GRAPH_PARAM_INVALID;
  }
  vector<GeTensorPtr> outputs_lower;
  auto ret = RunCpuKernelForValueRange(node, inputs, outputs_lower);
  if (ret != SUCCESS) {
    REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(), node->GetType().c_str());
    GELOGE(GRAPH_FAILED, "Calculate for node %s failed in constant folding", node->GetName().c_str());
    return GRAPH_FAILED;
  }

  inputs = ConstructInputTensors(node, false);
  if (inputs.empty()) {
    return GRAPH_PARAM_INVALID;
  }
  vector<GeTensorPtr> outputs_higher;
  ret = RunCpuKernelForValueRange(node, inputs, outputs_higher);
  if (ret != SUCCESS) {
    REPORT_INNER_ERROR("E19999", "Calculate for node %s(%s) failed", node->GetName().c_str(), node->GetType().c_str());
    GELOGE(GRAPH_FAILED, "Calculate for node %s failed in constant folding", node->GetName().c_str());
    return GRAPH_FAILED;
  }

  // construct value range from output tensor
  OpDescPtr node_desc = node->GetOpDesc();
  std::vector<std::pair<int64_t, int64_t>> output_tensor_value_range;
  size_t node_output_desc_size = node_desc->GetOutputsSize();
  for (size_t i = 0; i < node_output_desc_size; ++i) {
    output_tensor_value_range.clear();
    auto lower_tensor = outputs_lower[i];
    auto lower_tensor_shape_size = lower_tensor->GetTensorDesc().GetShape().GetShapeSize();
    auto higher_tensor = outputs_higher[i];
    auto higher_tensor_shape_size = higher_tensor->GetTensorDesc().GetShape().GetShapeSize();
    auto output_tensor_desc = node_desc->MutableOutputDesc(i);
    auto output_tensor_shape_size = output_tensor_desc->GetShape().GetShapeSize();
    if (output_tensor_shape_size != lower_tensor_shape_size || output_tensor_shape_size != higher_tensor_shape_size) {
      GELOGE(GRAPH_PARAM_INVALID, "Value range of output %s is invalid.", output_tensor_desc->GetName().c_str());
    }

    auto data_type = output_tensor_desc->GetDataType();
    switch (data_type) {
      GET_DATA_BY_DTYPE(DT_INT8, int8_t)
      GET_DATA_BY_DTYPE(DT_INT16, int16_t)
      GET_DATA_BY_DTYPE(DT_INT32, int32_t)
      GET_DATA_BY_DTYPE(DT_INT64, int64_t)
      GET_DATA_BY_DTYPE(DT_UINT8, uint8_t)
      GET_DATA_BY_DTYPE(DT_UINT16, uint16_t)
      GET_DATA_BY_DTYPE(DT_UINT32, uint32_t)
      GET_DATA_BY_DTYPE(DT_UINT64, uint64_t)
      GET_DATA_BY_DTYPE(DT_FLOAT, float)
      GET_DATA_BY_DTYPE(DT_DOUBLE, double)
      default:
        GELOGW("Data type:%s is not supported.", TypeUtils::DataTypeToSerialString(data_type).c_str());
        return GRAPH_FAILED;
    }
    output_tensor_desc->SetValueRange(output_tensor_value_range);
  }
  return GRAPH_SUCCESS;
}

template <typename T>
void InferValueRangePass::ConstructValueRange(const GeTensorPtr &left_tensor, const GeTensorPtr &right_tensor,
                                              std::vector<std::pair<int64_t, int64_t>> &value_range) {
  auto x = reinterpret_cast<const T *>(left_tensor->GetData().GetData());
  auto y = reinterpret_cast<const T *>(right_tensor->GetData().GetData());
  for (auto j = 0; j < left_tensor->GetTensorDesc().GetShape().GetShapeSize(); ++j) {
    auto left = static_cast<int64_t>(*(x + j));
    auto right = static_cast<int64_t>(*(y + j));
    value_range.emplace_back(std::make_pair(left, right));
  }
}
}  // namespace ge